Vascular Structure Identification in Intraoperative 3D Contrast-Enhanced Ultrasound Data

Validation of SART 3.5D algorithm for cerebrovascular dynamics and artery versus vein classification in presurgical 3D digital subtraction angiographies

Classification of arteries and veins in cerebral angiograms can increase the safety of neurosurgical procedures, such as StereoElectroEncephaloGraphy, and aid the diagnosis of vascular pathologies, as arterovenous malformations. We propose a new method for vessel classification using the contrast medium dynamics in rotational digital subtraction angiography (DSA). After 3D DSA and angiogram segmentation, contrast enhanced projections are processed to suppress soft tissue and bone structures attenuation effect and further enhance the CM flow. For each voxel labelled as vessel, a time intensity curve (TIC) is obtained as a linear combination of temporal basis functions whose weights are addressed by simultaneous algebraic reconstruction technique (SART 3.5D), expanded to include dynamics. Each TIC is classified by comparing the areas under the curve in the arterial and venous phases. Clustering is applied to optimize the classification thresholds. On a dataset of 60 patients, a median value of sensitivity (90%), specificity (91%), and accuracy (92%) were obtained with respect to annotated arterial and venous voxels up to branching order 4–5. Qualitative results are also presented about CM arrival time mapping and its distribution in arteries and veins respectively. In conclusion, this study shows a valuable impact, at no protocol extra-cost or invasiveness, concerning surgical planning related to the enhancement of arteries as major organs at risk. Also, it opens a new scope on the pathophysiology of cerebrovascular dynamics and its anatomical relationships.

Utilizing Intraoperative Navigated 3D Color Doppler Ultrasound in Glioma Surgery

Background

In glioma surgery, the patient’s outcome is dramatically influenced by the extent of resection and residual tumor volume. To facilitate safe resection, neuronavigational systems are routinely used. However, due to brain shift, accuracy decreases with the course of the surgery. Intraoperative ultrasound has proved to provide excellent live imaging, which may be integrated into the navigational procedure. Here we describe the visualization of vascular landmarks and their shift during tumor resection using intraoperative navigated 3D color Doppler ultrasound (3D iUS color Doppler).

Methods

Six patients suffering from glial tumors located in the temporal lobe were included in this study. Intraoperative computed tomography was used for registration. Datasets of 3D iUS color Doppler were generated before dural opening and after tumor resection, and the vascular tree was segmented manually. In each dataset, one to four landmarks were identified, compared to the preoperative MRI, and the Euclidean distance was calculated.

Results

Pre-resectional mean Euclidean distance of the marked points was 4.1 ± 1.3 mm (mean ± SD), ranging from 2.6 to 6.0 mm. Post-resectional mean Euclidean distance was 4.7. ± 1.0 mm, ranging from 2.9 to 6.0 mm.

Conclusion

3D iUS color Doppler allows estimation of brain shift intraoperatively, thus increasing patient safety. Future implementation of the reconstructed vessel tree into the navigational setup might allow navigational updating with further consecutive increasement of accuracy.

Brain Shift in Neuronavigation of Brain Tumors: An Updated Review of Intra-Operative Ultrasound Applications

Neuronavigation using pre-operative imaging data for neurosurgical guidance is a ubiquitous tool for the planning and resection of oncologic brain disease. These systems are rendered unreliable when brain shift invalidates the patient-image registration. Our previous review in 2015, Brain shift in neuronavigation of brain tumours: A review offered a new taxonomy, classification system, and a historical perspective on the causes, measurement, and pre- and intra-operative compensation of this phenomenon. Here we present an updated review using the same taxonomy and framework, focused on the developments of intra-operative ultrasound-based brain shift research from 2015 to the present (2020). The review was performed using PubMed to identify articles since 2015 with the specific words and phrases: “Brain shift” AND “Ultrasound”. Since 2015, the rate of publication of intra-operative ultrasound based articles in the context of brain shift has increased from 2–3 per year to 8–10 per year. This efficient and low-cost technology and increasing comfort among clinicians and researchers have allowed unique avenues of development. Since 2015, there has been a trend towards more mathematical advancements in the field which is often validated on publicly available datasets from early intra-operative ultrasound research, and may not give a just representation to the intra-operative imaging landscape in modern image-guided neurosurgery. Focus on vessel-based registration and virtual and augmented reality paradigms have seen traction, offering new perspectives to overcome some of the different pitfalls of ultrasound based technologies. Unfortunately, clinical adaptation and evaluation has not seen as significant of a publication boost. Brain shift continues to be a highly prevalent pitfall in maintaining accuracy throughout oncologic neurosurgical intervention and continues to be an area of active research. Intra-operative ultrasound continues to show promise as an effective, efficient, and low-cost solution for intra-operative accuracy management. A major drawback of the current research landscape is that mathematical tool validation based on retrospective data outpaces prospective clinical evaluations decreasing the strength of the evidence. The need for newer and more publicly available clinical datasets will be instrumental in more reliable validation of these methods that reflect the modern intra-operative imaging in these procedures.

Feasibility of Robot-Assisted Ultrasound Imaging with Force Feedback for Assessment of Thyroid Diseases

Medical ultrasound is extensively used to define tissue textures and to characterize lesions, and it is the modality of choice for detection and follow-up assessment of thyroid diseases. Classical medical ultrasound procedures are performed manually by an occupational operator with a hand-held ultrasound probe. These procedures require high physical and cognitive burden and yield clinical results that are highly operator-dependent, therefore frequently diminishing trust in ultrasound imaging data accuracy in repetitive assessment. A robotic ultrasound procedure, on the other hand, is an emerging paradigm integrating a robotic arm with an ultrasound probe. It achieves an automated or semi-automated ultrasound scanning by controlling the scanning trajectory, region of interest, and the contact force. Therefore, the scanning becomes more informative and comparable in subsequent examinations over a long-time span. In this work, we present a technique for allowing operators to reproduce reliably comparable ultrasound images with the combination of predefined trajectory execution and real-time force feedback control. The platform utilized features a 7-axis robotic arm capable of 6-DoF force-torque sensing and a linear-array ultrasound probe. The measured forces and torques affecting the probe are used to adaptively modify the predefined trajectory during autonomously performed examinations and probe-phantom interaction force accuracy is evaluated. In parallel, by processing and combining ultrasound B-Mode images with probe spatial information, structural features can be extracted from the scanning volume through a 3D scan. The validation was performed on a tissue-mimicking phantom containing thyroid features, and we successfully demonstrated high image registration accuracy between multiple trials.

The role of contrast-enhanced ultrasound in neurosurgical disease

Contrast-enhanced ultrasound (CEUS) is a relatively new imaging modality in the realm of neurosurgical disease. CEUS permits the examination of blood flow through arteries, veins, and capillaries via intravascular contrast agents and allows vascular architectural mapping with extreme sensitivity and specificity. While it has established utility in other organ systems such as the liver and kidneys, CEUS has not been studied extensively in the brain. This report presents a review of the literature on the neurosurgical applications of CEUS and provides an outline of the imaging modality’s role in the diagnosis, evaluation, and treatment of neurosurgical disease.

PCANet-Based Structural Representation for Nonrigid Multimodal Medical Image Registration

Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD) model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND), normalized mutual information (NMI), Weber local descriptor (WLD), and the sum of squared differences on entropy images (ESSD), the proposed method provides better registration performance in terms of target registration error (TRE) and subjective human vision.

Toward Optimal Computation of Ultrasound Image Reconstruction Using CPU and GPU

An ultrasound image is reconstructed from echo signals received by array elements of a transducer. The time of flight of the echo depends on the distance between the focus to the array elements. The received echo signals have to be delayed to make their wave fronts and phase coherent before summing the signals. In digital beamforming, the delays are not always located at the sampled points. Generally, the values of the delayed signals are estimated by the values of the nearest samples. This method is fast and easy, however inaccurate. There are other methods available for increasing the accuracy of the delayed signals and, consequently, the quality of the beamformed signals; for example, the in-phase (I)/quadrature (Q) interpolation, which is more time consuming but provides more accurate values than the nearest samples. This paper compares the signals after dynamic receive beamforming, in which the echo signals are delayed using two methods, the nearest sample method and the I/Q interpolation method. The comparisons of the visual qualities of the reconstructed images and the qualities of the beamformed signals are reported. Moreover, the computational speeds of these methods are also optimized by reorganizing the data processing flow and by applying the graphics processing unit (GPU). The use of single and double precision floating-point formats of the intermediate data is also considered. The speeds with and without these optimizations are also compared.